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Video Interviews (in English with French transcripts) Note: The opinions presented are not necessarily those of the Government of Canada.
(Video players are available here: QuickTime | Windows Media) Transcript: The Challenges of Data Collection My name is Simona Bignami. I’m an assistant professor at the University of Montreal in the Department of Demography. I recently moved to Montreal after completing my post-doctorate last year at Harvard University. My primary research since my Master’s and my PhD has been mostly concerned with demographic measurement—so all the problems relating to collecting demographic data and analyzing them, especially with relation to developing countries. Since I moved to Montreal, I’ve started exploring issues of Canadian and Québécois demography related to fertility, fertility decline and the condition of children in their life course. The outcome is to try to identify a very small set of indicators. For example, for mortality it is what we call life expectancy, which is the number of years that a person can expect to live from when they are born; and for fertility it is the average number of children that women might expect to bear in their life. These are sort of the two main outcome demographic measures. Now, the way to calculate these measures is not very easy and relies on all sorts of sources which tend to vary quite considerably between, once again, developed and developing countries. Developed countries have a statistical infrastructure—national broad statistics, they do a census year by year, they record how many people have been born or died. They mostly rely on their census and vital registration systems to calculate demographic indicators. Developing countries represent much more of a problem because, due to their political instability or their low level of development, they don’t tend to have such an infrastructure. So the way that demographers and population scientists have answered these problems is to collect surveys that basically consist of focusing on a subset of the population that they are interested in and which they consider to be representative of the general population. They go door to door or call person to person and ask these people questions about the things they are interested in. If you are interested in child health or fertility, you might ask a woman about her fertility history: how many children she has had, how many are alive, how many have died, and about each of the children. If you are interested in migration, you might ask where people lived at different stages in their life and the reasons why they moved around. In theory it seems very easy, but in practice it is not. These processes, I think, more than probably census and vital registration systems in developed countries, are prone to serious errors. I can tell you what my experience has been because I have done extensive data collection in Africa. The first time I went to Africa to do data collection, I didn’t know what demographic data collection really means. I was used to having all these numbers in my computer. I was playing with them; I was analyzing, summarizing and calculating demographic figures. Then, I arrived there and I realized that behind every number, there is a face, that behind what we call a household in demography there is a group of people who might live in a mud hut and have lots of problems that are very difficult to quantify with a couple of numbers. I also realized how disruptive it might be, to have a person who comes from outside and asks a person: could you please sit down with me for a couple of hours and I’m going to ask you a bunch of questions about your life, and what you do and what your children do. I think the most surprising discovery for me was to figure out that people have an agenda when they answer your questions. They don’t necessarily tell you the truth. And if they don’t tell you the truth, it might be because they are bored or because they don’t care. But in lots of cases, it might be because they want to get something out of you. I think this is the challenge of contemporary demography: that most people, when they look at demographics—its figures, its data—they think that it represents exactly the reality that we want it to represent. We don’t realize that in lots of cases that is really not true. Migration Measurements I can think of two very good examples. The first one is China. China used to be, up until the mid-1990s, a country where it was almost impossible for individuals to move, or to officially move, because they were linked to their registration district, which indicated where they had to live and where they had to work. The moment that these registration systems started breaking down and there was a little bit more liberalization about where people could live, there was a huge migration, especially from rural to urban areas. To give an idea of the numbers, we are talking about almost 200 million people moving around every year, either permanently or short term. On such a grand scale, even though China has a census system and a vital registration system, it is an elusive count. Because in most cases, if people are moving only temporarily, they would not change their residence; and if they are moving permanently they don’t have the incentive to change their residence, especially if they move individually and their family is remaining at the place where they used to live before. Another very good example about how difficult it is to measure migration is the migration between Mexico and the United States. At the beginning of the 20th century, the United States required manpower to build railroads and to do heavy work, so they started recruiting Mexicans from across the border. After that a sort of irregular migration from Mexico to the United Stated started. In lots of cases, these migrants are not legal. It is almost impossible to track them through ordinary means of data collection. A project that I know quite well is trying to deal with and to tackle the problem of measuring this migration. It is called the Mexican Migration Project and relates to the migration of Mexicans to the United States. This project has had an innovative approach. It is not just looking at the United States, which is the country of destination of this migration, but it is trying to go back to Mexico and to the communities that send these people abroad—legally or illegally, it doesn’t matter—and trying to identify all the different streams and to track where exactly this migration is coming from. It is of course very difficult to quantify in the aggregate. I’m sure that, even in a system where everything is perfectly regulated, there are going to be exceptions that make it tougher to identify who is moving, where and when. I think there are general rules for measuring migration, or other demographic processes, but the implementation depends on the context and the political incentives. For example, migration has always been heavily politicized. The legalization of illegal immigrants is often an instrument that is used by politicians to create or to increase the electoral base at a strategic moment. Of course, it is not that the definition of a migrant changes, but what changes is the way that politically and statistically migrants have been dealt with, which of course crucially affects the way that we can track migrants over time. The other big example in this respect is China, once again. The definition of a migrant from one census to the other has tended to change, as it has in a lot of other countries. So to try to reconstruct the migration stream over time is very difficult, because how we would define a migrant 20 years ago is not how we define a migrant right now. Variations of Data Collection How does data collection vary across contexts because of political reasons? As I said, I’m not sure I have the answer because there are certainly crucial differences in the way that demographic data collection systems are in different settings. We have some extreme cases, like data collection for China with its one-child policy, but for lots of other countries, we simply don’t know. I think that, for demographers, it would be very important to know more about what is behind the way that demographic data are collected because, as I was trying to say, the way data are collected crucially impacts the way that we interpret them and the way we use them to construct indicators. I don’t think that indicators per se will necessarily change, but I think that there are going to be, as there have been before, adjustments that stem from the fact that the data we collect over time have certainly augmented in quantity and, in some instances, improved in quality. I think that demography is a fairly stable discipline in terms of the models and the indicators that it uses. But I think that there is a certain flexibility, once again, to the quality and quantity of information that we collect. But I think that the bone structure of the discipline perhaps will not change. An example of the way that an indicator has been changed recently, due to different circumstances, is the total fertility rate. As I was saying before, the fertility rate measures the average number of children a woman might expect to bear. It is a measure, as we would say, that is cross-sectional: it refers to one specific year or to one specific period. So some demographers have started to argue that the decline in fertility that has been observed in Western Europe and other developed countries is not due to the fact that people overall, over their life, have fewer children, but that they postpone the decision of having children. So they start having children later. It would seem, as of today, that they are going to have fewer children in total, but they will recuperate later in their life. Demographers have devised a way to discount this timing effect into the total fertility rate and create the right indicators that more appropriately reflect the fertility trends over time. |
The Challenges of Data Collection